Self-Supervised Human Depth Estimation from Monocular Videos Article Swipe
Feitong Tan
,
Hao Zhu
,
Zhaopeng Cui
,
Siyu Zhu
,
Marc Pollefeys
,
Ping Tan
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.03358
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.03358
Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data. This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network. The self-supervised learning is achieved by minimizing a photo-consistency loss, which is evaluated between a video frame and its neighboring frames warped according to the estimated depth and the 3D non-rigid motion of the human body. To solve this non-rigid motion, we first estimate a rough SMPL model at each video frame and compute the non-rigid body motion accordingly, which enables self-supervised learning on estimating the shape details. Experiments demonstrate that our method enjoys better generalization and performs much better on data in the wild.
Related Topics
Concepts
Generalization
Computer science
Artificial intelligence
Monocular
Consistency (knowledge bases)
Ground truth
Motion (physics)
Computer vision
Frame (networking)
Motion estimation
Supervised learning
Pattern recognition (psychology)
Machine learning
Mathematics
Artificial neural network
Telecommunications
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2005.03358
- https://arxiv.org/pdf/2005.03358
- OA Status
- green
- Cited By
- 2
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3022363180
All OpenAlex metadata
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https://openalex.org/W3022363180Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2005.03358Digital Object Identifier
- Title
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Self-Supervised Human Depth Estimation from Monocular VideosWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-07Full publication date if available
- Authors
-
Feitong Tan, Hao Zhu, Zhaopeng Cui, Siyu Zhu, Marc Pollefeys, Ping TanList of authors in order
- Landing page
-
https://arxiv.org/abs/2005.03358Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2005.03358Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2005.03358Direct OA link when available
- Concepts
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Generalization, Computer science, Artificial intelligence, Monocular, Consistency (knowledge bases), Ground truth, Motion (physics), Computer vision, Frame (networking), Motion estimation, Supervised learning, Pattern recognition (psychology), Machine learning, Mathematics, Artificial neural network, Telecommunications, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2005.03358 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2005.03358 |
| publication_date | 2020-05-07 |
| publication_year | 2020 |
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